Deep Learning Driven Hypergraph Representation for Image-Based Emotion Recognition
Huang Yuchi; Lu Hanqing; Huang, Yuchi
2016
会议名称18th ACM International Conference on Multimodal Interaction
会议录名称国际会议
会议日期November 12–16, 2016
会议地点Tokyo, Japan
摘要
In this paper, we proposed a bi-stage framework for imagebased
emotion recognition by combining the advantages of
deep convolutional neural networks (D-CNN) and hypergraphs.
To exploit the representational power of D-CNN,
we remodeled its last hidden feature layer as the `attribute'
layer in which each hidden unit produces probabilities on a
speci c semantic attribute. To describe the high-order relationship
among facial images, each face was assigned to
various hyperedges according to the computed probabilities
on di erent D-CNN attributes. In this way, we tackled the
emotion prediction problem by a transductive learning approach,
which tends to assign the same label to faces that
share many incidental hyperedges (attributes), with the constraints
that predicted labels of training samples should be
similar to their ground truth labels. We compared the proposed
approach to state-of-the-art methods and its e ectiveness
was demonstrated by extensive experimentation.
关键词Emotion Recognition Hypergraph Deep Convolutional Networks
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12861
专题模式识别国家重点实验室_图像与视频分析
通讯作者Huang, Yuchi
推荐引用方式
GB/T 7714
Huang Yuchi,Lu Hanqing,Huang, Yuchi. Deep Learning Driven Hypergraph Representation for Image-Based Emotion Recognition[C],2016.
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